Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
OBPRM: an obstacle-based PRM for 3D workspaces
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
Ant algorithms for discrete optimization
Artificial Life
Robot Motion Planning
Evolving collective behavior in an artificial ecology
Artificial Life
Group Behaviors for Systems with Significant Dynamics
Autonomous Robots
Enhancing Randomized Motion Planners: Exploring with Haptic Hints
Autonomous Robots
Automatic Generation of Moving Crowds in the Virtual Environment
AMCP '98 Proceedings of the First International Conference on Advanced Multimedia Content Processing
Roadmap-Based Flocking for Complex Environments
PG '02 Proceedings of the 10th Pacific Conference on Computer Graphics and Applications
Better group behaviors in complex environments using global roadmaps
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Interaction and intelligent behavior
Interaction and intelligent behavior
Emergence of collective strategies in a prey-predator game model
Artificial Life
Map partitioning to approximate an exploration strategy in mobile robotics
Multiagent and Grid Systems
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While techniques exist for simulating swarming behaviors, these methods usually provide only simplistic navigation and planning capabilities. In this review, we explore the benefits of integrating roadmap-based path planning methods with flocking techniques to achieve different behaviors. We show how group behaviors such as exploring can be facilitated by using dynamic roadmaps (e.g., modifying edge weights) as an implicit means of communication between flock members. Extending ideas from cognitive modeling, we embed behavior rules in individual flock members and in the roadmap. These behavior rules enable the flock members to modify their actions based on their current location and state. We propose new techniques for several distinct group behaviors: homing, exploring (covering and goal searching), passing through narrow areas and shepherding. We present results that show that our methods provide significant improvement over methods that utilize purely local knowledge and moreover, that we achieve performance approaching that which could be obtained by an ideal method that has complete global knowledge. Animations of these behaviors can be viewed on our webpages.